
# Creating a control for picking a date

# https://dash.plotly.com/dash-core-components/datepickersingle

from datetime import date
import dash
from dash.dependencies import Input, Output
from dash import html
from dash import dcc
from jupyter_dash import JupyterDash

app = JupyterDash(__name__)
app.layout = html.Div([
    dcc.DatePickerSingle(
        id='my-date-picker-single',
        min_date_allowed=date(1995, 8, 5),
        max_date_allowed=date(2017, 9, 19),
        initial_visible_month=date(2017, 8, 5),
        date=date(2017, 8, 25)
    ),
    html.Div(id='output-container-date-picker-single')
])

@app.callback(
    Output('output-container-date-picker-single', 'children'),
    Input('my-date-picker-single', 'date'))
def update_output(date_value):
    string_prefix = 'You have selected: '
    if date_value is not None:

        #date.fromisoformat(date_string)
        #Return a date corresponding to a date_string given in the format YYYY-MM-DD:
        date_object = date.fromisoformat(date_value)

        date_string = date_object.strftime('%B %d, %Y')
        return string_prefix + date_string


if __name__ == '__main__':
    app.run_server(mode='inline', port=7865)


# Creating a control for picking a date
# https://strftime.org/
# https://dash.plotly.com/dash-core-components/datepickersingle

from datetime import date
import dash
from dash.dependencies import Input, Output
from dash import html
from dash import dcc
from jupyter_dash import JupyterDash

app = JupyterDash(__name__)
app.layout = html.Div([
    dcc.DatePickerSingle(
        id='my-date-picker-single',
        min_date_allowed=date(1995, 8, 5),
        max_date_allowed=date(2017, 9, 19),
        initial_visible_month=date(2017, 8, 5),
        date=date(2017, 8, 25)
    ),
    html.Div(id='output-container-date-picker-single')
])

@app.callback(
    Output('output-container-date-picker-single', 'children'),
    Input('my-date-picker-single', 'date'))
def update_output(date_value):
    string_prefix = 'You have selected: '
    if date_value is not None:

        #date.fromisoformat(date_string)
        #Return a date corresponding to a date_string given in the format YYYY-MM-DD:
        date_object = date.fromisoformat(date_value)

        #date_string = date_object.strftime('%Y-%m-%d')
        date_string = date_object.isoformat()
        return string_prefix + date_string


if __name__ == '__main__':
    app.run_server(mode='inline', port=8833)


# Filtering a dataset based on date range
import pandas as pd
from datetime import date

#Read the source data
df = pd.read_csv('Superstore_Orders_FIPS.csv', parse_dates=['Order Date'])
#df.head()

df_filtered = df[df['Order Date'].isin(pd.date_range("2018-01-01", "2018-06-30"))]
df_filtered.head(20)


# Filtering a dataset based on date range using date objects
import pandas as pd
from datetime import date

#Read the source data
df = pd.read_csv('Superstore_Orders_FIPS.csv', parse_dates=['Order Date'])

start_date = date(2018, 1, 1)
end_date = date(2018, 6, 30)

df_filtered = df[df['Order Date'].isin(pd.date_range(start_date, end_date))]
df_filtered.head(20)


# Filtering a dataset based on a date range using today's date
import pandas as pd
from datetime import date

#Read the source data
df = pd.read_csv('Superstore_Orders_FIPS.csv', parse_dates=['Order Date'])

start_date = date(2018, 1, 1)
end_date = date.today()

df_filtered = df[df['Order Date'].isin(pd.date_range(start_date, end_date))]
df_filtered.head(20)


# Finding the oldest date in the dataset
import pandas as pd
from datetime import date

#Read the source data
df = pd.read_csv('Superstore_Orders_FIPS.csv', parse_dates=['Order Date'])

#Find the oldest date in the dataset
print('---------------------Aggregated min date as a series')
min_date = df.agg({'Order Date': 'min'})
print(type(min_date))
print(min_date)

print('---------------------Get the date as the First item in the series')
min_date = df.agg({'Order Date': 'min'})
min_date = min_date.iat[0]
print(type(min_date))
print(min_date)

print('---------------------Get the date as the First item in the series, date part only')
min_date = df.agg({'Order Date': 'min'}).dt.date
min_date = min_date.iat[0]
print(type(min_date))
print(min_date)

---------------------Aggregated min date as a series
<class 'pandas.core.series.Series'>
Order Date   2018-01-03
dtype: datetime64[ns]
---------------------Get the date as the First item in the series
<class 'pandas._libs.tslibs.timestamps.Timestamp'>
2018-01-03 00:00:00
---------------------Get the date as the First item in the series, date part only
<class 'datetime.date'>
2018-01-03


# Creating a control for picking a date, using an end date from the data set and today's date

from datetime import date
import dash
from dash.dependencies import Input, Output
from dash import html
from dash import dcc
from jupyter_dash import JupyterDash

#Read the data
df = pd.read_csv("Superstore_Orders_FIPS.csv", parse_dates=["Order Date"])

#Get the oldest date in the datset , date part only
min_date = df.agg({'Order Date': 'min'}).dt.date
min_date = min_date.iat[0]

app = JupyterDash(__name__)
app.layout = html.Div([
    dcc.DatePickerSingle(
        id='my-date-picker-single',
        min_date_allowed=min_date,
        max_date_allowed=date.today(),
        initial_visible_month=date.today(),
        date=date.today()
    ),
    html.Div(id='output-container-date-picker-single')
])

@app.callback(
    Output('output-container-date-picker-single', 'children'),
    Input('my-date-picker-single', 'date'))
def update_output(date_value):
    string_prefix = 'You have selected: '
    if date_value is not None:

        #date.fromisoformat(date_string)
        #Return a date corresponding to a date_string given in the format YYYY-MM-DD:
        date_object = date.fromisoformat(date_value)

        #date_string = date_object.strftime('%Y-%m-%d')
        date_string = date_object.isoformat()   #another format to define date
        return string_prefix + date_string


if __name__ == '__main__':
    app.run_server(mode='inline', port = 7865)


# Creating a bar graph

import pandas as pd
from datetime import date
import dash
from dash.dependencies import Input, Output
from dash import html
from dash import dcc
from jupyter_dash import JupyterDash
import plotly.express as px

#Read the data
df = pd.read_csv("Superstore_Orders_FIPS.csv", parse_dates=["Order Date"])

#Get the oldest date in the datset , date part only
min_date = df.agg({'Order Date': 'min'}).dt.date
min_date = min_date.iat[0]

app = JupyterDash(__name__)
app.layout = html.Div([
    dcc.DatePickerSingle(
        id='my-date-picker-single',
        min_date_allowed=min_date,
        max_date_allowed=date.today(),
        initial_visible_month=date.today(),
        date=date.today()
    ),
    html.Div(id='output-container-date-picker-single')
])

df_aggregated = df.groupby(['Category'], as_index=False).agg({'Sales':'sum'})
fig = px.bar(df_aggregated, x='Category', y='Sales')
fig.show()         
        
@app.callback(
    Output('output-container-date-picker-single', 'children'),
    Input('my-date-picker-single', 'date'))
def update_output(date_value):
    string_prefix = 'You have selected: '
    if date_value is not None:
        

        #date.fromisoformat(date_string)
        #Return a date corresponding to a date_string given in the format YYYY-MM-DD:
        date_object = date.fromisoformat(date_value)

        #date_string = date_object.strftime('%Y-%m-%d')
        date_string = date_object.isoformat()   #another format to define date
        return string_prefix + date_string


if __name__ == '__main__':
    app.run_server(mode='inline', port=8790)


# Creating a dash graph object

import pandas as pd
from datetime import date
import dash
from dash.dependencies import Input, Output
from dash import html
from dash import dcc
from jupyter_dash import JupyterDash
import plotly.express as px

#Read the data
df = pd.read_csv("Superstore_Orders_FIPS.csv", parse_dates=["Order Date"])

#Get the oldest date in the datset , date part only
min_date = df.agg({'Order Date': 'min'}).dt.date
min_date = min_date.iat[0]

app = JupyterDash(__name__)
app.layout = html.Div([
    dcc.DatePickerSingle(
        id='my-date-picker-single',
        min_date_allowed=min_date,
        max_date_allowed=date.today(),
        initial_visible_month=date.today(),
        date=date.today()
    ),
    dcc.Graph(id='bar-chart')
])
        
        
@app.callback(
    Output('bar-chart', 'figure'),
    Input('my-date-picker-single', 'date'))
def update_output(date_value):
    string_prefix = 'You have selected: '
    if date_value is not None:
        
        df_aggregated = df.groupby(['Category'], as_index=False).agg({'Sales':'sum'})
        fig = px.bar(df_aggregated, x='Category', y='Sales')
        return fig

        #date.fromisoformat(date_string)
        #Return a date corresponding to a date_string given in the format YYYY-MM-DD:
        #date_object = date.fromisoformat(date_value)

        #date_string = date_object.strftime('%Y-%m-%d')
        #date_string = date_object.isoformat()   #another format to define date

        #return string_prefix + date_string


if __name__ == '__main__':
    app.run_server(mode='inline', port=9876)


#Hooking up the graph

from datetime import date
import dash
from dash.dependencies import Input, Output
from dash import html
from dash import dcc
from jupyter_dash import JupyterDash
import plotly.express as px

#Read the source data
df = pd.read_csv('Superstore_Orders_FIPS.csv', parse_dates=['Order Date'])

# Get the oldest date in the dataset, date part only
min_date = df.agg({'Order Date': 'min'}).dt.date
min_date = min_date.iat[0]

app = JupyterDash(__name__)
app.layout = html.Div([
    dcc.DatePickerSingle(
        id='my-date-picker-single',
        min_date_allowed=min_date,
        max_date_allowed=date.today(),
        initial_visible_month=date.today(),
        date=date.today()
    ),
    dcc.Graph(id='bar-chart')
])

@app.callback(
    Output('bar-chart', 'figure'),
    Input('my-date-picker-single', 'date'))
def update_output(date_value):
    string_prefix = 'You have selected: '
    if date_value is not None:
        start_date = date.fromisoformat(date_value)
        end_date= date.today()
        
        #Filter on the date range
        df_filter = df[df['Order Date'].isin(pd.date_range(start_date, end_date))]
        
        df_aggregated = df_filter.groupby(['Category'], as_index=False).agg({'Sales':'sum'})
        fig = px.bar(df_aggregated, x='Category', y='Sales')
        return fig
        

if __name__ == '__main__':
    app.run_server(mode='inline', port=9123)


#Hooking up the graph

from datetime import date, datetime, timedelta
from datetime import date
import dash
from dash.dependencies import Input, Output
from dash import html
from dash import dcc
from jupyter_dash import JupyterDash
import plotly.express as px
import pandas as pd

#Read the source data
df = pd.read_csv('Superstore_Orders_FIPS.csv', parse_dates=['Order Date'])

# Get the oldest date in the dataset, date part only
min_date = df.agg({'Order Date': 'min'}).dt.date
min_date = min_date.iat[0]

app = JupyterDash(__name__)
app.layout = html.Div([
    dcc.DatePickerSingle(
        id='my-date-picker-single',
        min_date_allowed=min_date,
        max_date_allowed=date.today(),
        initial_visible_month=date.today() + timedelta(weeks=-8),
        date=date.today() + timedelta(weeks=-8)
    ),
    dcc.Graph(id='bar-chart')
])

@app.callback(
    Output('bar-chart', 'figure'),
    Input('my-date-picker-single', 'date'))
def update_output(date_value):
    string_prefix = 'You have selected: '
    if date_value is not None:
        start_date = date.fromisoformat(date_value)
        end_date= date.today()
        
        #Filter on the date range
        df_filter = df[df['Order Date'].isin(pd.date_range(start_date, end_date))]
        
        df_aggregated = df_filter.groupby(['Category'], as_index=False).agg({'Sales':'sum'})
        fig = px.bar(df_aggregated, x='Category', y='Sales')
        return fig
        

if __name__ == '__main__':
    app.run_server(mode='inline', port=8121)


#Hooking up the graph

from datetime import date, datetime, timedelta
from datetime import date
import dash
from dash.dependencies import Input, Output
from dash import html
from dash import dcc
from jupyter_dash import JupyterDash
import plotly.express as px
import pandas as pd

#Read the source data
df = pd.read_csv('Superstore_Orders_FIPS.csv', parse_dates=['Order Date'])

# Get the oldest date in the dataset, date part only
min_date = df.agg({'Order Date': 'min'}).dt.date
min_date = min_date.iat[0]

app = JupyterDash(__name__)
app.layout = html.Div([
    dcc.DatePickerSingle(
        id='my-date-picker-single',
        min_date_allowed=min_date,
        max_date_allowed=date.today(),
        initial_visible_month=date.today() + timedelta(weeks=-8),
        date=date.today() + timedelta(weeks=-8)
    ),
    dcc.Graph(id='bar-chart')
])

@app.callback(
    Output('bar-chart', 'figure'),
    Input('my-date-picker-single', 'date'))
def update_output(date_value):
    if date_value is not None:
        start_date = date.fromisoformat(date_value)
        end_date = date.today()

        # Filter on the date range
        df_filter = df[df['Order Date'].isin(pd.date_range(start_date, end_date))]
        
        # Group by both Category and Sub-Category
        df_aggregated = df_filter.groupby(['Category', 'Sub-Category'], as_index=False).agg({'Sales': 'sum'})
        
        # Create the bar chart with color differentiation for Sub-Category
        fig = px.bar(df_aggregated, x='Category', y='Sales', color='Sub-Category')
        return fig


if __name__ == '__main__':
    app.run_server(mode='inline', port=8867)

	Category	City	Country/Region	Order Date	Postal Code	FIPS	Region	Segment	State	State-Code	Sub-Category	Discount	Profit	Quantity	Sales
5	Furniture	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Furnishings	0.0	14.1694	7	48.860
6	Office Supplies	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Art	0.0	1.9656	4	7.280
7	Technology	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Phones	0.2	90.7152	6	907.152
8	Office Supplies	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Binders	0.2	5.7825	3	18.504
9	Office Supplies	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Appliances	0.0	34.4700	5	114.900
10	Furniture	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Tables	0.2	85.3092	9	1706.184
11	Technology	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Phones	0.2	68.3568	4	911.424
17	Office Supplies	West Jordan	United States	2018-05-13	84084.0	49035.0	West	Consumer	Utah	UT	Storage	0.0	9.9900	2	55.500
157	Furniture	Seattle	United States	2018-03-01	98115.0	53033.0	West	Consumer	Washington	WA	Chairs	0.2	51.4764	2	457.568
195	Office Supplies	Hamilton	United States	2018-03-21	45011.0	39017.0	East	Consumer	Ohio	OH	Art	0.2	1.2038	2	7.408
196	Office Supplies	Hamilton	United States	2018-03-21	45011.0	39017.0	East	Consumer	Ohio	OH	Art	0.2	1.5876	3	6.048
244	Furniture	Lakeville	United States	2018-06-01	55044.0	27037.0	Central	Home Office	Minnesota	MN	Chairs	0.0	580.5394	7	2001.860
245	Office Supplies	Lakeville	United States	2018-06-01	55044.0	27037.0	Central	Home Office	Minnesota	MN	Storage	0.0	41.6800	2	166.720
246	Office Supplies	Lakeville	United States	2018-06-01	55044.0	27037.0	Central	Home Office	Minnesota	MN	Paper	0.0	23.9400	6	47.880
247	Office Supplies	Lakeville	United States	2018-06-01	55044.0	27037.0	Central	Home Office	Minnesota	MN	Appliances	0.0	496.0725	5	1503.250
248	Office Supplies	Lakeville	United States	2018-06-01	55044.0	27037.0	Central	Home Office	Minnesota	MN	Paper	0.0	12.4416	4	25.920
306	Office Supplies	Lakewood	United States	2018-06-01	8701.0	34029.0	East	Home Office	New Jersey	NJ	Binders	0.0	20.9208	3	45.480
307	Office Supplies	Lakewood	United States	2018-06-01	8701.0	34029.0	East	Home Office	New Jersey	NJ	Art	0.0	83.8680	6	289.200
340	Office Supplies	Philadelphia	United States	2018-04-13	19140.0	42101.0	East	Corporate	Pennsylvania	PA	Art	0.2	1.1160	4	17.856
341	Office Supplies	Philadelphia	United States	2018-04-13	19140.0	42101.0	East	Corporate	Pennsylvania	PA	Binders	0.7	-407.9760	10	509.970

	Category	City	Country/Region	Order Date	Postal Code	FIPS	Region	Segment	State	State-Code	Sub-Category	Discount	Profit	Quantity	Sales
5	Furniture	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Furnishings	0.0	14.1694	7	48.860
6	Office Supplies	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Art	0.0	1.9656	4	7.280
7	Technology	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Phones	0.2	90.7152	6	907.152
8	Office Supplies	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Binders	0.2	5.7825	3	18.504
9	Office Supplies	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Appliances	0.0	34.4700	5	114.900
10	Furniture	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Tables	0.2	85.3092	9	1706.184
11	Technology	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Phones	0.2	68.3568	4	911.424
17	Office Supplies	West Jordan	United States	2018-05-13	84084.0	49035.0	West	Consumer	Utah	UT	Storage	0.0	9.9900	2	55.500
157	Furniture	Seattle	United States	2018-03-01	98115.0	53033.0	West	Consumer	Washington	WA	Chairs	0.2	51.4764	2	457.568
195	Office Supplies	Hamilton	United States	2018-03-21	45011.0	39017.0	East	Consumer	Ohio	OH	Art	0.2	1.2038	2	7.408
196	Office Supplies	Hamilton	United States	2018-03-21	45011.0	39017.0	East	Consumer	Ohio	OH	Art	0.2	1.5876	3	6.048
244	Furniture	Lakeville	United States	2018-06-01	55044.0	27037.0	Central	Home Office	Minnesota	MN	Chairs	0.0	580.5394	7	2001.860
245	Office Supplies	Lakeville	United States	2018-06-01	55044.0	27037.0	Central	Home Office	Minnesota	MN	Storage	0.0	41.6800	2	166.720
246	Office Supplies	Lakeville	United States	2018-06-01	55044.0	27037.0	Central	Home Office	Minnesota	MN	Paper	0.0	23.9400	6	47.880
247	Office Supplies	Lakeville	United States	2018-06-01	55044.0	27037.0	Central	Home Office	Minnesota	MN	Appliances	0.0	496.0725	5	1503.250
248	Office Supplies	Lakeville	United States	2018-06-01	55044.0	27037.0	Central	Home Office	Minnesota	MN	Paper	0.0	12.4416	4	25.920
306	Office Supplies	Lakewood	United States	2018-06-01	8701.0	34029.0	East	Home Office	New Jersey	NJ	Binders	0.0	20.9208	3	45.480
307	Office Supplies	Lakewood	United States	2018-06-01	8701.0	34029.0	East	Home Office	New Jersey	NJ	Art	0.0	83.8680	6	289.200
340	Office Supplies	Philadelphia	United States	2018-04-13	19140.0	42101.0	East	Corporate	Pennsylvania	PA	Art	0.2	1.1160	4	17.856
341	Office Supplies	Philadelphia	United States	2018-04-13	19140.0	42101.0	East	Corporate	Pennsylvania	PA	Binders	0.7	-407.9760	10	509.970

	Category	City	Country/Region	Order Date	Postal Code	FIPS	Region	Segment	State	State-Code	Sub-Category	Discount	Profit	Quantity	Sales
0	Furniture	Henderson	United States	2020-11-08	42420.0	21059.0	South	Consumer	Kentucky	KY	Bookcases	0.00	41.9136	2	261.9600
1	Furniture	Henderson	United States	2020-11-08	42420.0	21059.0	South	Consumer	Kentucky	KY	Chairs	0.00	219.5820	3	731.9400
2	Office Supplies	Los Angeles	United States	2020-06-12	90036.0	6037.0	West	Corporate	California	CA	Labels	0.00	6.8714	2	14.6200
3	Furniture	Fort Lauderdale	United States	2019-10-11	33311.0	12011.0	South	Consumer	Florida	FL	Tables	0.45	-383.0310	5	957.5775
4	Office Supplies	Fort Lauderdale	United States	2019-10-11	33311.0	12011.0	South	Consumer	Florida	FL	Storage	0.20	2.5164	2	22.3680
5	Furniture	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Furnishings	0.00	14.1694	7	48.8600
6	Office Supplies	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Art	0.00	1.9656	4	7.2800
7	Technology	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Phones	0.20	90.7152	6	907.1520
8	Office Supplies	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Binders	0.20	5.7825	3	18.5040
9	Office Supplies	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Appliances	0.00	34.4700	5	114.9000
10	Furniture	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Tables	0.20	85.3092	9	1706.1840
11	Technology	Los Angeles	United States	2018-06-09	90032.0	6037.0	West	Consumer	California	CA	Phones	0.20	68.3568	4	911.4240
12	Office Supplies	Concord	United States	2021-04-15	28027.0	37025.0	South	Consumer	North Carolina	NC	Paper	0.20	5.4432	3	15.5520
13	Office Supplies	Seattle	United States	2020-12-05	98103.0	53033.0	West	Consumer	Washington	WA	Binders	0.20	132.5922	3	407.9760
14	Office Supplies	Fort Worth	United States	2019-11-22	76106.0	48439.0	Central	Home Office	Texas	TX	Appliances	0.80	-123.8580	5	68.8100
15	Office Supplies	Fort Worth	United States	2019-11-22	76106.0	48439.0	Central	Home Office	Texas	TX	Binders	0.80	-3.8160	3	2.5440
16	Office Supplies	Madison	United States	2018-11-11	53711.0	55025.0	Central	Consumer	Wisconsin	WI	Storage	0.00	13.3176	6	665.8800
17	Office Supplies	West Jordan	United States	2018-05-13	84084.0	49035.0	West	Consumer	Utah	UT	Storage	0.00	9.9900	2	55.5000
18	Office Supplies	San Francisco	United States	2018-08-27	94109.0	6075.0	West	Consumer	California	CA	Art	0.00	2.4824	2	8.5600
19	Technology	San Francisco	United States	2018-08-27	94109.0	6075.0	West	Consumer	California	CA	Phones	0.20	16.0110	3	213.4800